Image processing apparatus and image processing method

ABSTRACT

An image processing apparatus has a stochastic resonance processing unit executing a stochastic resonance processing to obtain a result. The result corresponds to a result that is calculated in a case where each of a plurality of pixel signals constituting reading image data is added noise and subjected to a binary processing and a plurality of results obtained by parallelly performing above step are synthesized and the parallel number is infinite. The stochastic resonance processing unit sets, with regard to a pixel signal as a processing target among the plurality of pixel signals, at least one of a strength of the noise and a threshold value used for the binary processing based on a pixel signal of the input image data corresponding to the pixel signal.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to an image processing apparatus and animage processing method to detect a singular portion included in adetection target image.

Description of the Related Art

In order to extract a detection target signal from an input signalburied in noise, a stochastic resonance phenomenon is useful. Thestochastic resonance phenomenon is a phenomenon in which an input signalburied in noise is further added with noise and the resultant signal issubsequently subjected to nonlinear processing to thereby emphasize adetection target signal. However, in such a stochastic resonancephenomenon, a correlation coefficient used as an evaluation valueshowing the performance of the detection result changes depending on thestrength of the added noise as shown in FIG. 1. In the case of FIG. 1,the correlation coefficient is maximum when the added noise strength is30. That is, the noise strength is desirably tuned because of theexistence of the noise strength optimal for the realization of themaximum detection accuracy.

J. J. Collins, Carson C. Chow and Thomas T. Imhoff, “Stochasticresonance without tuning”, NATURE, (UK), 20 Jul. 1995, vol. 376, pp.236-238 (hereinafter referred to as Non-Patent Document 1) discloses aconfiguration as shown in FIG. 2 in which an input signal I(x) isbranched to a plurality of pieces and different noises are added to therespective pieces and the resultant pieces are subjected to nonlinearprocessing to further synthesize the outputs thereof to thereby detect adetection target signal at a stable accuracy. Non-Patent Document 1describes that the increase of the branches allows the correlationcoefficient to be stabilized regardless of the strength, whicheliminates the peak as shown in FIG. 1, thus resulting in theelimination of the need to tune the noise strength. Japanese PatentLaid-Open No. 2013-135244 discloses a configuration in which independentnoise generation sources as in Non-Patent Document 1 are not preparedand noise generated by one noise generation source is added by beingmutually delayed by a plurality of signal lines, thereby providing thesame effect as that of Non-Patent Document 1.

Japanese Patent Laid-Open No. 2011-52991 discloses a method to set anonlinear function as a logistic function, a sigmoid function, or ahyperbolic tangent function to thereby increase the correlationcoefficient within a wide noise strength range. In the case of JapanesePatent Laid-Open No. 2011-52991 as described above, there is no need toprepare a plurality of nonlinear circuits as in Non-Patent Document 1and Japanese Patent Laid-Open No. 2013-135244. Thus, an effect similarto those of the above publications can be realized by a simpler circuit.

In recent years, the extraction of a detection target signal using thestochastic resonance phenomenon as described above also may be used forproduct inspection or the like. For example, an inspection target can beimaged and the resultant image data is added with predetermined noiseand the resultant data is subjected to nonlinear processing, therebyextracting a singular portion such as a flaw existing in the image.Furthermore, the singular portion extraction mechanism as describedabove is not limited to the inspection step in a production site butalso can be used for a product itself. Specific examples include aconfiguration wherein a personal printer images an image printed byitself to compare image data used for the printing with the image dataobtained by reading the printed image to automatically extract asingular portion such as ejection failure.

However, when an actual image is printed and a singular portion existingin the image is extracted, securing the extraction accuracy of thesingular portion has been difficult even by the use of the above patentpublication. In the case of an image including the combination ofvarious lightness and hues such as a photograph image in particular, howeasy a singular portion can be extracted is different depending on thelightness or hue of the pixel, which has caused a case where theextraction frequency of the singular portion may be uneven depending onthe image position. Specifically, there has been a case where a wrongpoint is unintendedly extracted in the same image even when the point isactually not a singular portion or an actually-singular portion cannotbe extracted.

SUMMARY OF THE INVENTION

The present invention has been made in order to solve the abovedisadvantage. Thus, it is an objective of the invention to provide animage processing apparatus and an image processing method by which asingular portion can be extracted at a stable accuracy from an imageincluding therein various lightness and hues.

According to a first aspect of the present invention, there is providedan image processing apparatus, comprising: a unit configured to acquirereading image data composed of a plurality of pixel signals by imagingan image that is printed by a printing unit based on input image datacomposed of a plurality of pixel signals; a stochastic resonanceprocessing unit configured to execute a stochastic resonance processingin which each of the plurality of pixel signals constituting the readingimage data is added noise and subjected to a binary processing and aplurality of results obtained by parallelly performing above step aresynthesized; and an output unit configured to output the result of thestochastic resonance processing, wherein the stochastic resonanceprocessing unit sets, with regard to a pixel signal as a processingtarget among the plurality of pixel signals, at least one of a strengthof the noise and a threshold value used for the binary processing basedon a pixel signal of the input image data corresponding to the pixelsignal.

According to a second aspect of the present invention, there is providedan image processing apparatus, comprising: a unit configured to acquirereading image data composed of a plurality of pixel signals by imagingan image printed by a printing unit based on input image data composedof a plurality of pixel signals; a stochastic resonance processing unitconfigured to execute a stochastic resonance processing to obtain aresult corresponding to a result that is calculated in a case where eachof the plurality of pixel signals constituting the reading image data isadded noise and subjected to a binary processing and a plurality ofresults obtained by parallelly performing above step are synthesized andthe parallel number is infinite; and an output unit configured to outputthe result of the stochastic resonance processing, wherein thestochastic resonance processing unit sets, with regard to a pixel signalas a processing target among the plurality of pixel signals, at leastone of a strength of the noise and a threshold value used for the binaryprocessing based on a pixel signal of the input image data correspondingto the pixel signal.

According to a third aspect of the present invention, there is providedimage processing method, comprising: a step of acquire reading imagedata composed of a plurality of pixel signals by imaging an imageprinted based on input image data composed of a plurality of pixelsignals; stochastic resonance processing step of executing a stochasticresonance processing in which each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized; and an output step of outputtingthe result of the stochastic resonance processing, wherein thestochastic resonance processing step sets, with regard to a pixel signalas a processing target among the plurality of pixel signals, at leastone of a strength of the noise and a threshold value used for the binaryprocessing based on a pixel signal of the input image data correspondingto the pixel signal.

According to a fourth aspect of the present invention, there is providedan image processing method, comprising: a step of acquiring readingimage data composed of a plurality of pixel signals by imaging an imageprinted based on input image data composed of a plurality of pixelsignals; stochastic resonance processing step of executing a stochasticresonance processing to obtain a result corresponding to a result thatis calculated in a case where each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized and the parallel number isinfinite; and an output step of outputting the result of the stochasticresonance processing, wherein the stochastic resonance processing stepsets, with regard to a pixel signal as a processing target among theplurality of pixel signals, at least one of a strength of the noise anda threshold value used for the binary processing based on a pixel signalof the input image data corresponding to the pixel signal.

According to a fifth aspect of the present invention, there is provideda non-transitory computer-readable storage medium which stores a programfor allowing a computer to execute a image processing method, the imageprocessing method comprising: a step of acquiring reading image datacomposed of a plurality of pixel signals by imaging an image printedbased on input image data composed of a plurality of pixel signals; astochastic resonance processing step of executing a stochastic resonanceprocessing to obtain a result corresponding to a result that iscalculated in a case where each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized and the parallel number isinfinite; and an output step of outputting the result of the stochasticresonance processing, wherein the stochastic resonance processing stepsets, with regard to a pixel signal as a processing target among theplurality of pixel signals, at least one of a strength of the noise anda threshold value used for the binary processing based on a pixel signalof the input image data corresponding to the pixel signal.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the relation between an added noise strength and acorrelation coefficient in a stochastic resonance processing;

FIG. 2 illustrates the stochastic resonance processing in Non-PatentDocument 1;

FIGS. 3A to 3D show an embodiment of an image processing apparatus thatcan be used in the present invention;

FIG. 4 is a block diagram to explain the configuration of the control inthe first embodiment;

FIG. 5 is a schematic view illustrating the configuration of a fullline-type inkjet printing apparatus;

FIGS. 6A and 6B illustrate the arrangement configuration of printingelements of a printing head and reading elements of a reading head;

FIGS. 7A and 7B are a diagram to explain a white stripe due to defectiveejection;

FIGS. 8A to 8D illustrate input image data and reading image data;

FIGS. 9A to 9C are a diagram to explain a method of setting a thresholdvalue and a strength;

FIG. 10 is a flowchart illustrating a singular portion detectionalgorithm in the first embodiment;

FIGS. 11A to 11D illustrate the input image data and the reading imagedata;

FIGS. 12A and 12B are a diagram to explain a method of setting athreshold value and the strength;

FIGS. 13A and 13B illustrate the histogram of the random number N;

FIGS. 14A and 14B illustrate the formula 8 and the formula 9 by graphs;

FIG. 15 is a flowchart illustrating a singular portion detectionalgorithm in the third embodiment; and

FIGS. 16A and 16B illustrate a serial-type inkjet printing apparatus.

DESCRIPTION OF THE EMBODIMENTS

FIGS. 3A to 3D illustrate the embodiment of an image processingapparatus 1 that can be used in the present invention. The imageprocessing apparatus of the present invention is used to subject imagedimage data to a popup processing to allow a user to more easilyrecognize a white stripe in a printed image for example or a processingfor the determination by the apparatus itself. The image processingapparatus of the present invention can take various system forms.

FIG. 3A illustrates an embodiment in which the image processingapparatus 1 includes a reading unit 2. For example, this correspond to acase where a sheet on which a predetermined image is printed by aninkjet printing apparatus is placed on a reading base of the readingunit 2 in the image processing apparatus 1 and is imaged by an opticalsensor for example and the image data is processed by an imageprocessing unit 3. The image processing unit 3 includes a CPU or animage processing accelerator providing a processing having a higherspeed than that of the CPU and controls the reading operation by thereading unit 2 and subject received image data to a predeterminedinspection processing for example.

FIG. 3B illustrates an embodiment in which the image processingapparatus 1 is externally connected to a reading apparatus 2A includingthe reading unit 2. For example, this corresponds to a system in which ascanner is connected to a PC for example. A general connection methodsuch as USB, GigE, or CameraLink may be used. The image data read by thereading unit 2 is provided via an interface 4 to the image processingunit 3. The image processing unit subjects the received image data to apredetermined inspection processing. In the case of this embodiment, theimage processing apparatus 1 also may be further externally connected toa printing apparatus 5A including a printing unit 5.

FIG. 3C illustrates an embodiment in which the image processingapparatus 1 includes the reading unit 2 and the printing unit 5. Thiscorresponds to a complex machine including a scanner function, a printerfunction, and an image processing function for example. The imageprocessing unit 3 controls all operations such as the printing operationin the printing unit 5, the reading operation in the reading unit 2, andthe inspection processing to an image read by the reading unit 2.

FIG. 3D illustrates an embodiment in which a complex machine 6 includingthe reading unit 2 and the printing unit 5 is externally connected tothe image processing apparatus 1. This corresponds to a system in whicha complex machine including both of a scanner function and a printerfunction is connected to a PC for example. The image processingapparatus 1 of the present invention also can take any of the formsshown in FIGS. 3A to 3D. However, the following section will describethe image inspection apparatus using the embodiment of FIG. 3D.

First Embodiment

FIG. 4 is a block diagram for explaining the control configuration inthe embodiment of FIG. 3D. The image processing apparatus 1 as a signalextraction processing apparatus consists of a host PC for example. A CPU301 executes various kinds of processing while using a RAM 302 as a workarea in accordance with a program retained in an HDD 303. For example,the CPU 301 generates image data that can be printed by the complexmachine 6 based on a command received from a user via a keyboard/mouseI/F 305 or a program retained by the HDD 303 and transfers this to thecomplex machine 6. The CPU 301 subjects the image data received from thecomplex machine 6 via a data transfer I/F 304 to predeterminedprocessing based on the program stored in the HDD to display the resultor various pieces of information on a not-shown display via a displayI/F 306. Image data I(x), which is a target of the stochastic resonanceprocessing of this embodiment as described later, is received from thecomplex machine 6 via the data transfer I/F 304.

On the other hand, in the complex machine 6, a CPU 311 executes variouskind of processing while using a RAM 312 as a work area based on aprogram retained by a ROM 313. The complex machine 6 includes an imageprocessing accelerator 309 for performing high-speed image processing, ascanner controller 307 for controlling the reading unit 2, and a headcontroller 314 for controlling the printing unit 5.

The image processing accelerator 309 is hardware that can execute imageprocessing at a higher speed than the CPU 311. The image processingaccelerator 309 is activated by allowing the CPU 311 to write parametersrequired for the image processing and data to predetermined address ofthe RAM 312. After the above parameters and data are read, the data issubjected to a predetermined image processing. However, the imageprocessing accelerator 309 is not an indispensable element. Thus,similar processing can be executed by the CPU 311.

The head controller 314 supplies printing data to a printing head 100provided in the printing unit 5 and controls the printing operation ofthe printing head 100. The head controller 314 is activated by allowingthe CPU 311 to write printing data that can be printed by the printinghead 100 and control parameters to a predetermined address of the RAM312 and executes ejecting operation based on the printing data.

The scanner controller 307 outputs, while controlling the individualreading elements arranged in the reading unit 2, RGB brightness dataobtained therefrom to the CPU 311. The CPU 311 transfers the resultantRGB brightness data via the data transfer I/F 310 to the imageprocessing apparatus 1. The data transfer I/F 304 of the imageprocessing apparatus 1 and the data transfer I/F 310 of the complexmachine 6 can be connected by a USB, IEEE1394, or LAN for example.

FIG. 5 is a schematic view illustrating the configuration of an inkjetprinting apparatus that can be used as the complex machine 6 of thisembodiment (hereinafter also may be simply referred to as a printingapparatus). The printing apparatus of this embodiment is a fullline-type printing apparatus in which the printing head 100 having awidth similar to that of the sheet P that may be a printing medium or aninspection target and the reading head 107 are parallelly arranged in aY direction. The printing head 100 includes four printing elementcolumns 101 to 104 through which inks of black (K), cyan (c), magenta(M), and yellow (Y) are ejected, respectively. These printing elementcolumns 101 to 104 are parallelly arranged in a conveying direction ofthe sheet P (Y direction). At a further downstream of the printingelement columns 101 to 104, the reading head 107 is provided. Thereading head 107 includes therein a plurality of reading elements forreading a printed image arranged in the X direction.

In order to perform printing processing or reading processing, the sheetP is conveyed at a predetermined speed in accordance with the rotationof a conveying roller 105 in the Y direction of the drawing. During thisconveyance, the printing processing by the printing head 100 or thereading processing by the reading head 107 is performed. The sheet P ata position at which the printing processing by the printing head 100 orthe reading processing by the reading head 107 is performed is supportedfrom the lower side by a platen 106 consisting of a flat plate tothereby maintain the distance from the printing head 100 or the readinghead 107 and the smoothness.

FIGS. 6A and 6B illustrate the arrangement configuration of printingelements in the printing head 100 and the arrangement configuration ofreading elements in the reading head 107. In the printing head 100, theprinting element columns 101 to 104 corresponding to the respective inkcolors are configured so that a plurality of printing element substrates201 in which a plurality of printing elements 108 are arranged at afixed pitch are alternately arranged in the Y direction so as to becontinuous in the X direction while having the overlap region D. To thesheet P conveyed at a fixed speed in the Y direction, ink is ejectedthrough the individual printing elements 108 based on the printing dataat a fixed frequency, thereby printing an image having the resolutioncorresponding to the arrangement pitch of the printing element 108 ontothe sheet P. If some defect such as ejection failure or a shifting ofejection direction occurs on a specific printing element 108, a whitestripe or a black stripe extending in the Y direction appears on thesheet P.

FIGS. 7A and 7B are diagrams to explain a white stripe caused by adefective ejection of a printing element that should be extracted as asingular portion in particular in this embodiment. FIGS. 7A and 7Billustrate the layout showing how printing elements 108 are arranged inone of the printing element columns 101 to 104 shown in FIG. 6A and thelayout of dots printed on the sheet P by the individual printingelements 108. FIG. 7A illustrates a status in which any printing element108 has no defective ejection while FIG. 7B illustrates a status inwhich some printing elements 108 have a defective ejection. In a casewhere some printing elements 108 have defective ejection, as shown inFIG. 7B, in the regions to be printed by the printing elements, no dotis placed, causing white stripes extending in the Y direction to appearon the sheet P. This embodiment intends to securely extract such whitestripes as singular portions.

On the other hand, the reading head 107 includes a plurality of readingsensors 109 arranged at a predetermined pitch in the X direction.Although not shown, the individual reading sensors 109 are arranged sothat a plurality of reading elements that may be the minimum unit of areading pixel are arranged in the X direction. The reading element ofthis embodiment outputs a multivalued brightness signal of red (R),green (G), and blue (B) as reading data. The image on the sheet Pconveyed at a fixed speed in the Y direction can be imaged by thereading elements of the individual reading sensor 109 at a predeterminedfrequency to thereby read the entire image printed on the sheet P at anarrangement pitch of the reading elements.

FIGS. 8A to 8D illustrate image data printed by a printing elementcolumn and image data read by the reading head 107. FIG. 8A illustratesan example of image data printed by a printing element column. Therespective gradation 1 to gradation 4 have lightness (densities)different from each other. In the drawings, the X direction correspondsto the direction along which printing elements are arranged while the Ydirection corresponds to the direction along which the sheet P isconveyed.

FIG. 8B illustrates the distribution of brightness signals in such imagedata. The term brightness signal has a value obtained by substitutingRGB signal values owned by image data in a formula 4 described later.The horizontal axis shows the direction along which printing elementsare arranged while the vertical axis shows brightness signal values Scorresponding to the respective printing elements. Assuming that thebrightness signal value at the gradation 1 is S1, the brightness signalvalue at the gradation 2 is S2, the brightness signal value at thegradation 3 is S3, and the brightness signal value at the gradation 4 isS4, then a relation of S1<S2<S3<S4 is established.

On the other hand, FIG. 8C illustrates an example of image data obtainedby reading, by the reading head 107, an actual image printed by theprinting head 100 based on the image data shown in FIG. 8A. A case isshown in which an ejection failure occurs at a printing element of aregion corresponding to the gradation 2 and a white stripe extending inthe Y direction exits.

FIG. 8D illustrates brightness signal values of the reading image datashown in FIG. 8C. The brightness signal values are also obtained bysubstituting RGB signal values owned by reading image data in theformula 4 described later. Since various noises are added during aprinting operation and a reading operation, reading data have differentbrightness signal values for the respective pixels even at the samegradation. Thus, the signal value distribution shown in FIG. 8D has adifference in gradation unclearer than the case of the signal valuedistribution (S1, S2, S3, and S4) shown in FIG. 8B and also hasunclearer white stripe positions. However, it can be seen that thebrightness signal values have fluctuation ranges dislocated amonggradations and that the white stripe positions have fluctuation rangesrelatively higher than the fluctuation ranges of other positions. Thisembodiment has an objective of comparing the image data as shown in FIG.8A with the reading data as shown in FIG. 8C to securely extract asingular portion such as a white stripe.

By the way, when attention is paid on FIGS. 8C and 8D, the white striperegion has brightness signals that is higher than brightness signals atsurrounding gradations 2 but that are not higher than the brightnesssignals of the entire gradation 4. Specifically, if a threshold value toextract a white stripe in the gradation 2 is equal to a threshold valueto extract a white stripe in the gradation 4, then alarger-than-required-number of singular portions are unintendedlyextracted from regions included in the gradation 3 or the gradation 4.Although not clearly shown in the drawings, strength of noise addedduring a printing operation and a reading operation also may bedifferent depending on a gradation. Thus, it is assumed that anappropriate strength value of noise added in the stochastic resonanceprocessing also may be different depending on the gradation. In view ofthe above, through keen researches, the present inventors have reached afinding that it is useful to adjust a threshold value to use andstrength of noise added to execute the stochastic resonance processingdepending on original image data for each pixel.

The following section will specifically describe a singular portiondetection algorithm in this embodiment. The singular portion detectionalgorithm of this embodiment is an algorithm to print an actual imagebased on input image data to compare reading image data obtained byreading the actual image with the input image data to thereby extract asingular portion such as a white stripe. This embodiment is not limitedto an inkjet printing apparatus as the complex machine 6. However, thefollowing description will be made based on an assumption that an imageprinted by the printing head 100 of the complex machine 6 is read by thereading head 107 of the same complex machine. First, the followingsection will describe the stochastic resonance processing used in thisembodiment.

Reference is made again to FIG. 2 illustrating the concept of theprocessing using the stochastic resonance phenomenon also disclosed inNon-Patent Document 1. A processing target signal I(x) is a valueobtained from image data read by a reading sensor 109. The referencenumeral x shows the pixel position. The processing target signal I(x) isbranched to M pieces and each of pieces is processed parallelly asfollow. Each of the pieces is added with different noise N(x,m)×K. m isa parameter showing one of M branch paths and is an integer in the rangefrom 1 to M. N(x,m) shows a random number corresponding to the branchpath m of the pixel position x and has the range from 0 to 1. The valueN(x,m)×K obtained by multiplying the random number N(x,m) by the noisestrength K as an integer is added to the processing target signal I(x).That is, assuming that the signal value after the addition of noise isi(x,m), then the following formula can be obtained.

i(x,m)=I(x)+N(x,m)×K  (Formula 1)

By comparing the signal value i(x,m) after the noise addition with apredetermined threshold value T, nonlinear processing (binaryprocessing) is performed to thereby obtain a binary signal j(x,m).Specifically, the following is established.

i(x,m)T→j(x,m)=1

i(x,m)<T→j(x,m)=0  (Formula 2)

Thereafter, M pieces of binary signals j(x,m) are synthesized and issubjected to an average processing. The resultant value is set as thesignal value J after the stochastic resonance. Specifically, thefollowing is established.

$\begin{matrix}{{J(x)} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}\; {j\left( {x,m} \right)}}}} & \left( {{Formula}\mspace{14mu} 3} \right)\end{matrix}$

In this embodiment, in the processing of Non-Patent Document 1 asdescribed above, the noise strength K and the threshold value T areadjusted depending on original image data inputted to the printing head100.

FIGS. 9A to 9C are a diagram to explain the concept of a method ofsetting the threshold value T and the noise strength K in a case wherethe image data as shown in FIG. 8A is inputted. FIG. 9A illustrates acommon threshold value T set for the gradation 1 to the gradation 4. Inthis case, in order to substantially equalize the probabilities at whichthe common threshold value T is exceeded in the respective gradations,the noise strengths K at the respective gradations must be adjusted.Specifically, such a noise strength K is set that allows a difference Cbetween a value obtained by adding the noise strength K to thebrightness signal value S and the threshold value T has the same valueat the respective gradations. That is, assuming that the gradation 1 hasa noise strength K1, the gradation has a noise strength K2, thegradation 3 has a noise strength K3, and the gradation 4 has a noisestrength K4, then the following relation is established under conditionsin which C and T are a constant.

T=(S1+K1)+C

T=(S2+K2)+C

T=(S3+K3)+C

T=(S4+K4)+C

That is, since S1<S2<S3<S4 is established, K1>K2>K3>K4 is established.

FIG. 9B illustrates a case where the common noise strength K is set atthe gradation 1 to gradation 4. In order to substantially equalize theprobabilities at which the threshold value is exceeded at the respectivegradations, the threshold value T at the respective gradation must beadjusted. Specifically, assuming that the gradation 1 has a thresholdvalue T1, the gradation 2 has a threshold value T2, the gradation 3 hasa threshold value T3, and the gradation 4 has a threshold value T4, thenthe following relation is established under condition in which C and Kare a constant.

T1=(S1+K)+C

T2=(S2+K)+C

T3=(S3+K)+C

T4=(S4+K)+C

That is, since S1<S2<S3<S4 is established, T1<T2<T3<T4 is established.

FIG. 9C shows a case where the gradation 1 to gradation 4 have thresholdvalues T and noise strengths K different from one another. For example,even when similar defective ejections exist, a white stripe is lessconspicuous in a region having a low gradation (or a high lightness)than in a region having a high gradation (or a low lightness), thusreducing the need to extract the white stripe as a singular portion. Insuch a case, instead of equalizing the probabilities at which thethreshold value is exceeded as in FIGS. 9A and 9B, the probabilities aredesirably adjusted depending on the gradations. In this case, thedifference C between a value obtained by adding the noise strength K tothe signal value S and the threshold value T is a value singular to eachgradation.

T1=(S1+K1)+C1

T2=(S2+K2)+C2

T3=(S3+K4)+C3

T4=(S4+K4)+C4

FIG. 10 is a flowchart to explain the singular portion detectionalgorithm executed by the CPU 301 of this embodiment. When thisprocessing is started, then the CPU 301 in Step S1 allows the printingunit 5 to print an actual image. Specifically, the CPU 301 accesses theCPU 311 of the complex machine 6 to cause it to supply the sheet P intothe apparatus. Then, based on received input image data, the printinghead 100 is caused to print the actual image via the head controller314.

Next, in Step S2, the actual image printed in Step S1 is read by thereading unit 2. Specifically, the scanner controller 307 is driven toobtain output signals from a plurality of reading elements arranged inthe reading sensor 109 to acquire reading image data corresponding topixel positions (x). The input image data received in Step S1 and thereading image data acquired in Step S2 are both multivalued RGB data.The CPU 301 stores these pieces of data in the RAM 312 by as pixelsignals corresponding to the pixel positions (x).

In Step S3, the CPU 301 initializes the parameters n and m (x=1, m=1). nshows a processing target pixel. m on the other hand shows one of Mbranch paths arranged in FIG. 2.

In Step S4, the CPU 301 calculates, based on the input image datareceived in Step S1 and the reading image data acquired in Step S2, thebrightness signal value of the pixel (x) as a processing target by usingformula 4. Hereinafter, the brightness signal corresponding to the pixel(x) of the input image data is represented as an input brightness signalvalue S(x) while the brightness signal corresponding to the pixel (x) ofthe reading image data is represented as a processing target signalvalue I(x).

S(x)=Ri(x)×0.3+Gi(x)×0.6+Bi(x)×0.1

I(x)=Rr(x)×0.3+Gr(x)×0.6+Br(x)×0.1  (Formula 4)

In the formulae, Ri(x), Gi(x), and Bi(x) show the RGB signal values ofthe input image data corresponding to the pixel (x), respectively, andRr(x), Gr(x), and Br(x) show the RGB signal values of the reading imagedata, respectively. If these pieces of RGB data have a bit number of 8bits, then S(x) and I(x) are in the range from 0 to 255 and, if thesepieces of RGB data have a bit number of 16 bits, then S(x) and I(x) arein the range from 0 to 65535. In this embodiment, an example will bedescribed in which these pieces of RGB data are 8 bits (0 to 255). Theweighting coefficient (0.3, 0.6, 0.1) multiplied with the respectivesignal values RGB are an example and also can be appropriately adjusteddepending on the feature of a to-be-extracted singular portion, an inkcolor to be used, or the color of the sheet for example.

In Step S5, the CPU 301 sets, based on the input brightness signal valueI(x), the threshold value T and the noise strength K for using in thestochastic resonance processing. The threshold value T and the noisestrength K can be set based on various concepts as described for FIGS.9A to 9C. The setting method may be the one to refer a table stored in amemory for example in which the threshold value T and the noise strengthK to be set are associated with the value of the input brightness signalS(x) or the one to calculate the values by using some formula. The tableor formula may be prepared for each concept as described for FIGS. 9A to9C.

In Step S6, the CPU 301 calculates the signal value i(x,m) after thenoise addition based on the formula 1. Specifically, a random numberN(x,m) singular to (x,m) is generated and is multiplied with the noisestrength K set in Step S5. Then, the resultant value is added to theprocessing target signal I(x) obtained in Step S4.

i(x,m)=I(x)+N(x,m)×K  (Formula 1)

In this embodiment, the random number N(x,m) show white noisesubstantially uniformly generated in the range from 0 to 1.

In Step S7, the CPU 301 compares the threshold value T set in Step S5with the signal value i(x,m) calculated in Step S6 to perform the binaryprocessing based on the formula 2, resulting in the binary data j (x,m)having a value of 1 or 0.

Next, in Step S8, the CPU 301 determines whether or not m=M isestablished. In a case where m<M is established, the parameter m isincremented in Step S9 and the processing returns to Step S6 to processa branch path not yet subjected to the stochastic resonance processing.In a case where m=M is established on the other hand, this means thatj(x,m) is obtained for all M branch paths. Thus, the processing proceedsto Step S10 to acquire the signal value J(x) after the stochasticresonance based on the formula 3.

Next, in Step S11, whether or not the parameter n reaches a maximumvalue. In a case where n does not reach the maximum value, then in StepS12 the CPU 301 increments the parameter n and returns the parameter mto the initial value. Then, the CPU 301 returns to Step S4 in order tosubject the next pixel (x) to the stochastic resonance processing. Onthe other hand, in a case where the CPU 301 determines that theparameter n reaches the maximum value that is the CPU 301 determinesthat all pixels are completely subjected to the stochastic resonanceprocessing, in Step S11, then the CPU 301 proceeds to Step S13.

In Step S13, the CPU 301 performs the judgment processing based on thestochastic resonance data J(x) obtained in Step S10 to extract singularportions. The judgment processing performed in Step S13 is not limitedto a particular method. For example, the stochastic resonance data J(x)may be compared with the judgment threshold value D prepared in advanceto extract J(x) exceeding the judgment threshold value D as a singularportion. Alternatively, an average value of J(x) may be calculated forall pixels to extract portions having J(x) excessively higher than thisaverage value as singular portions. Then, this processing is completed.The display apparatus connected via the display I/F 306 may displaypixels having a value equal to or higher than a predetermined thresholdvalue so that the pixels can be observed by the inspector or also maydirectly display the stochastic resonance data J(x). Then, thisprocessing is completed.

According to the above-described embodiment, the noise strength K andthe threshold value T used for the stochastic resonance processing areset for each pixel based on input image data of a pixel as a processingtarget. This can consequently allow singular portions to be stablyextracted from an actual image including various gradations.

Second Embodiment

The second embodiment is similar to the first embodiment in that theimage processing systems described for FIG. 4 to FIG. 6B are used. In acase where the printing head as shown in FIG. 6A is used in which manyprinting elements are arranged with a high density, even when there arerelatively many printing elements having defective ejections, a stripein an actual image is not so conspicuous and thus may not be recognizedas a defect. Specifically, the conspicuousness of the white stripedepends on the density (gradation) of the actual image, the number andthe distribution of printing elements having defective ejections, in aprinting element column. For example, even in the case of the sameprinting element column, a white stripe is less conspicuous in an imageprinted using a region including many printing elements having defectiveejections (high density) than in an image printed using a regionincluding a very few printing elements having defective ejections (lowdensity), thus causing a case where the need to extract a singularportion is eliminated. On the other hand, the number and positions ofthe printing elements having defective ejections in a printing elementcolumn also can be detected in advance. As can be seen from the above,in this embodiment, the noise strength K and the threshold value T usedfor the stochastic resonance processing to extract singular portions inan actual image are set depending on the brightness signal S(x) of theinput image data and the ejection status of a region including pixels asa processing target. Methods to detect the ejection status include, forexample, the one to confirm an image obtained by printing apredetermined pattern or the one to confirm the ejection operationstatus by a sensor.

FIGS. 11A to 11D illustrate an example of image data to be printed by aprinting element column and image data read by the reading head 107 asin FIGS. 8A to 8D. It is assumed that S1<S2 is established when thebrightness signal value at the gradation 1 is S1 and the brightnesssignal value at the gradation 2 is S2. The gradation 1 and the gradation2 include a region including a relatively few defective ejections and aregion including a relatively many defective ejections, respectively. Inthe drawings, a region in the gradation 1 having a relatively fewdefective ejection is a region 1-1, a region in the gradation 1 having arelatively many defective ejections is a region 1-2, a region in thegradation 2 having a relatively few defective ejections is a region 2-1,and a region in the gradation 2 having a relatively many defectiveejections is a region 2-2. In view of the situation as described above,in this embodiment, the noise strength K and the threshold value T usedin the stochastic resonance processing are set based on the brightnesssignal S(x) and the ejection status of a printing element regionincluding a pixel as a processing target.

FIGS. 12A and 12B are a diagram to explain the concept of the method ofsetting the threshold value T and the noise strength K in a similarmanner as in FIGS. 9A to 9C. FIG. 12A shows a case where the commonthreshold value T is set for all regions. In this case, because theprocessing target signal values based on the reading image data aredispersed as shown in FIG. 11D, in order to substantially equalize theprobabilities where the threshold value is exceeded in the respectiveregions, there is need to adjust the noise strength K for the respectivegradations. For example, assuming that the region 1-1 has a noisestrength K1-1, the region 1-2 has a noise strength K1-2, the region 2-1has a noise strength K2-1, and the gradation 2-2 has a noise strengthK2-2, then the following relation is established.

K1-1>K1-2>K2-1>K2-2

FIG. 12B shows a case where the common noise strength K is set for allregions. In order to substantially equalize the probabilities where thethreshold value is exceeded in all regions, there is need to adjust thethreshold value T for the respective region. For example, assuming thatthe region 1-1 has a threshold value T1-1, the region 1-2 has athreshold value T1-2, the region 2-1 has a threshold value T2-1, and theregion 2-2 has a threshold value T2-2, then the following relation isestablished.

T1-1<T1-2<T2-1<T2-2

This embodiment is similar to the first embodiment in that the singularportion detection algorithm can be executed based on the flowchartdescribed for FIG. 10. However, in Step S5, the noise strength K and thethreshold value T are set based on the brightness signal S(x) in a pixelof a processing target and a printing element characteristiccorresponding to the pixel of the processing target. The setting methodmay be, as in the first embodiment, the one to refer a table stored in amemory for example in which the threshold value T and the noise strengthK to be set are associated with the value of the brightness signal andthe printing element characteristic or the one to calculate the valuesby using some formula.

In the above description, a configuration has been used in which, sincean inkjet printing apparatus is used, the ejection status of anindividual printing element is detected in advance. However, even whenan image is printed by other methods such as a heat transfer method, theeffect of this embodiment can be obtained so long as the printing statusof the individual printing element is acquired in advance.

Third Embodiment

According to Non-Patent Document 1, the higher value M is preferred inthe stochastic resonance processing described in the formula 1 toformula 3. An increase of the value M allows the signal value J(x) to becloser to a value showing the probability at which the processing targetsignal value I(x) of each pixel exceeds the binary threshold value T inthe nonlinear processing. In other words, deriving a formula forcalculating the probability at which the processing target signal valueI(x) exceeds the binary threshold value T allows, without requiring manynoise addition processing or nonlinear processing as shown in FIG. 2, adetection processing equivalent thereto. In this embodiment, such asingle formula is derived in advance to use this formula to realize aneffect similar to that of the above embodiment. Thus, the followingsection will firstly describe the probability at which the processingtarget signal I(x) exceeds the binary threshold value T.

FIGS. 13A and 13B illustrate a histogram that is convergent in a casewhere infinity many random numbers N are generated. The horizontal axisshows the random number N in the range from 0 to 1. The vertical axisshows the probability f(N) at which each value N occurs. FIG. 13Aillustrates a case where the average value is 0.5 and the normaldistribution based on 3σ=1 is used and FIG. 13B illustrates a case wherethe random number N in the range of 0 to 1 is generated at the samefrequency (so-called white noise). The following description will bemade based on the assumption that the random number N is generated basedon such a distribution.

According to the formula 1 and formula 2, the probability at which theresult after the binarization of the individual pixel is j(x,m)=1 isequal to the probability at which:

I(x)+N(x,m)×K≧T is established.

Assuming that K(strength) has a positive value, then the above formulacan be expressed as below.

N(x,m)≧{T−I(x)}/K  (Formula 5)

Assuming that the right side is A, then the following formula can beestablished.

N(x,m)≧A  (Formula 6)

The probability at which the result j(x,m) of the individual pixel afterthe binarization is j(x,m)=1 that is, the signal value J(x) after thestochastic resonance processing, is a probability that the formula 6 issatisfied. In the respective diagrams of FIGS. 13A and 13B, the areas ofthe shaded areas correspond to this probability and can be representedby the following formula.

$\begin{matrix}{{J(x)} = \left\{ \begin{matrix}1 & {A < 0} \\0 & {A > 1} \\{1 - {\int_{N = 0}^{A}{{f(N)}{dN}}}} & {0 \leqq A \leqq 1}\end{matrix} \right.} & \left( {{Formula}\mspace{14mu} 7} \right)\end{matrix}$

In the case where the histogram for the generation of the random numberN has a normal distribution as shown in FIG. 13A, then the formula 7 isrepresented as shown below.

${J(x)} = \left\{ \begin{matrix}1 & {A < 0} \\0 & {A > 1} \\{1 - \frac{1}{1 + {\exp \left\{ {- {\alpha \left( {A - 0.5} \right)}} \right\}}}} & {0 \leqq A \leqq 1}\end{matrix} \right.$

In a case where the histogram for the noise N has the normaldistribution of ±3σ=1 as shown in FIG. 13A, then the coefficient α isabout α=10.8. When the constant A is returned to the original formula{T−I(x,m)}/K, then the formula 8 is represented as shown below.

$\begin{matrix}{{J(x)} = \left\{ \begin{matrix}1 & {T < {I(x)}} \\0 & {{I(x)} < {T - K}} \\{1 - \frac{1}{1 + {\exp \left\{ {- {\alpha \left( {\frac{\left( {T - {I(x)}} \right)}{K} - 0.5} \right)}} \right\}}}} & {{T - K} \leqq {I(x)} \leqq T}\end{matrix} \right.} & \left( {{Formula}\mspace{14mu} 8} \right)\end{matrix}$

In a case where the histogram for the generation of the random number Nis as shown in FIG. 13B on the other hand, then the formula 7 can berepresented as shown below.

${J(x)} = \left\{ \begin{matrix}1 & {A < 0} \\0 & {A > 1} \\{1 - A} & {0 \leqq A \leqq 1}\end{matrix} \right.$

When the constant A is returned to the original formula {T−I(x)}/K, theformula is represented by formula 9 as below.

$\begin{matrix}{{J(x)} = \left\{ \begin{matrix}1 & {T < {I(x)}} \\0 & {{I(x)} < {T - K}} \\{1 - {\left( {T - {I(x)}} \right)/K}} & {{T - K} \leqq {I(x)} \leqq T}\end{matrix} \right.} & \left( {{Formula}\mspace{14mu} 9} \right)\end{matrix}$

FIGS. 14A and 14B illustrate the formula 8 and formula 9 by graphs. Byusing the formula 8 or formula 9 under appropriate noise strength K andthreshold value T, a singular portion can be extracted at such anaccuracy that is the same as an accuracy at which the method ofNon-Patent Document 1 is used to set a branch number M to the detectiontarget signal value I(x) at infinity.

In this embodiment, while using the image processing system describedfor FIG. 4 to FIG. 6B as in the first embodiment, any of the formula 8or formula 9 is used instead of the M parallel column processingdescribed in the first embodiment as a singular portion detectionalgorithm.

FIG. 15 is a flowchart for explaining the singular portion detectionalgorithm executed by the CPU 301 of this embodiment. The followingsection will only describe steps different from those of the flowchartof FIG. 10 described in the first embodiment.

In Step S30, the CPU 301 initializes the parameter x (x=1). In Step S60,the CPU 301 substitutes the inspection target signal value I(x)calculated in Step S4 into I(x) of the formula 8 or formula 9 and usesthe noise strength K and the threshold value T set in Step S5 tocalculate the signal value J(x) after the stochastic resonanceprocessing. Thereafter, the processing after Step S11 may be performedas in the first embodiment.

The embodiment described above allows, without requiring many nonlinearcircuits, a singular portion to be stably extracted from an actual imageincluding various gradations.

OTHER EMBODIMENTS

The above description has been made for an example in which the fullline-type inkjet printing apparatus shown in FIG. 5 is used as thecomplex machine 6. However, the present invention is not limited such anembodiment. A printing unit using the serial type inkjet printingapparatus as shown in FIGS. 16A and 16B also can be used.

In FIG. 16A, the printing head 170 is reciprocated in the X direction inthe drawing while being provided on a carriage 171. During this travel,four printing element columns eject inks of black (K), cyan (c), magenta(M), and yellow (Y) respectively. When such printing scanning iscompleted, the sheet P is conveyed in the Y direction by a distancecorresponding to the printing width of the printing head 170. Byalternately repeating the printing scanning and the conveying operationas described above, an image is formed on the sheet P. On the otherhand, the reading head 107 is composed of a plurality of readingelements arranged in the X direction as in FIG. 5.

In a case where the serial type inkjet printing apparatus as in FIG. 16Aincludes a printing element having a defective ejection, a white stripeextends in the X direction as shown in FIG. 16B. Another white stripecaused by the conveying operation also extends in the X direction. Thatis, in the case of the serial-type printing apparatus, a stripe causedby a printing element having a defective ejection and a stripe caused byan error in the conveying operation similarly appear in the samedirection. Even in such a case, a singular portion can be stablyextracted from an actual image by using appropriate threshold value andnoise strength depending on the input image data to perform the abovestochastic resonance processing.

Although the above description has been made based on an example inwhich a white stripe is caused by a defective ejection, the aboveembodiment also can be used to extract singular portions having abrightness value lower than those of the surrounding points such as ablack stripe or density unevenness caused by excessive ejection. Even insuch a case, an effect similar to that of the embodiment can be obtainedby setting appropriate threshold value T and noise strength K dependingon the input image data to use the threshold value T and noise strengthK to perform the stochastic resonance processing.

In the above description, in view of the fact that the conspicuous of awhite stripe depends on the gradation (gray density), the RGB signals ofthe image data are substituted in (formula 4) and the threshold value Tand noise strength K are set based on the calculated brightness signalS. However, the present invention is not limited to such an embodiment.The brightness signal S(x) and processing target signal I(x) used in theabove embodiment also can be calculated not only based on the linearfunction as in the formula 4 but also based on other functions such as amultidimensional function.

In the above description, in Step S4, the RGB signals of reading imagedata are substituted in the formula 4 to thereby calculate theprocessing target signal I(n). However, the processing target signalI(n) in the stochastic resonance processing after Step S6 also can beset as a difference between the reading image data and the input imagedata. In this case, the processing target signal I(n) can be calculatedby the following formula.

I(x) = (Rr(x) − Ri(x)) × 0.3 + (Gr(x) − Gi(x)) × 0.6 + (Br(x) − Bi(x)) × 0.1

Furthermore, a system has been illustratively described in which thecomplex machine 6 is connected to the image processing apparatus 1 asshown in FIG. 4. However, the present invention is not limited to suchan embodiment.

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2016-071182 filed Mar. 31, 2016, which is hereby incorporated byreference wherein in its entirety.

What is claimed is:
 1. An image processing apparatus, comprising: a unitconfigured to acquire reading image data composed of a plurality ofpixel signals by imaging an image that is printed by a printing unitbased on input image data having a plurality of pixel signals; astochastic resonance processing unit configured to execute a stochasticresonance processing in which each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized; and an output unit configured tooutput the result of the stochastic resonance processing, wherein thestochastic resonance processing unit sets, with regard to a pixel signalas a processing target among the plurality of pixel signals, at leastone of a strength of the noise and a threshold value used for the binaryprocessing based on a pixel signal of the input image data correspondingto the pixel signal.
 2. An image processing apparatus, comprising: aunit configured to acquire reading image data composed of a plurality ofpixel signals by imaging an image printed by a printing unit based oninput image data having a plurality of pixel signals; a stochasticresonance processing unit configured to execute a stochastic resonanceprocessing to obtain a result corresponding to a result that iscalculated in a case where each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized and the parallel number isinfinite equivalency; and an output unit configured to output the resultof the stochastic resonance processing, wherein the stochastic resonanceprocessing unit sets, with regard to a pixel signal as a processingtarget among the plurality of pixel signals, at least one of a strengthof the noise and a threshold value used for the binary processing basedon a pixel signal of the input image data corresponding to the pixelsignal.
 3. The image processing apparatus according to claim 2, whereinthe stochastic resonance processing unit calculates a difference betweenthe plurality of pixel signals constituting the reading image data andthe plurality of pixel signals constituting the input image data to addthe noise to the difference to subject the difference to the binaryprocessing.
 4. The image processing apparatus according to claim 2,wherein the respective pixel signals constituting the input image dataare composed of R, G, and B brightness signals; and the stochasticresonance processing unit sets at least one of the strength of the noiseand the threshold value used in the binary processing based on abrightness signal obtained by synthesizing the R, G, and B brightnesssignals.
 5. The image processing apparatus according to claim 2, whereinthe respective pixel signals constituting the reading image data arecomposed of R, G, and B brightness signals; and the stochastic resonanceprocessing unit performs the stochastic resonance processing on abrightness signal obtained by synthesizing the R, G, and B brightnesssignals.
 6. The image processing apparatus according to claim 2, whereinthe printing unit prints the image using a plurality of printingelements; and the stochastic resonance processing unit sets at least oneof the strength of the noise and the threshold value based on a pixelsignals of the input image data and the printing status of the printingelement corresponding to a pixel signal as the processing target.
 7. Theimage processing apparatus according to claim 6, wherein the printingunit is an inkjet printing apparatus; and the image processing apparatusfurther comprising a unit configured to detect an ink ejection status ofthe printing element as the printing status.
 8. The image processingapparatus according to claim 2, wherein the printing unit is an inkjetprinting apparatus including a plurality of printing elements; and thesingular portion is a white stripe caused by ejection failure of theprinting element.
 9. The image processing apparatus according to claim2, wherein the noise is white noise.
 10. The image processing apparatusaccording to claim 2, wherein the predetermined stochastic resonanceprocessing is performed by using the following formula to calculate dataJ(x) obtained by processing input data I(x),${J(x)} = \left\{ \begin{matrix}1 & {T < {I(x)}} \\0 & {{I(x)} < {T - K}} \\{1 - {\left( {T - {I(x)}} \right)/K}} & {{T - K} \leqq {I(x)} \leqq T}\end{matrix} \right.$ where T is a threshold value to quantize the inputdata and K is a noise strength.
 11. The image processing apparatusaccording to claim 2, further comprising: an extraction unit configuredto extract a singular portion based on the result of the stochasticresonance processing.
 12. The image processing apparatus according toclaim 2, further comprising: a reading unit configured to image theimage printed by the printing unit.
 13. The image processing apparatusaccording to claim 12, further comprising the printing unit.
 14. Animage processing method, comprising: a step of acquire reading imagedata composed of a plurality of pixel signals by imaging an imageprinted based on input image data composed of a plurality of pixelsignals; a stochastic resonance processing step of executing astochastic resonance processing in which each of the plurality of pixelsignals constituting the reading image data is added noise and subjectedto a binary processing and a plurality of results obtained by parallellyperforming above step are synthesized; and an output step of outputtingthe result of the stochastic resonance processing, wherein thestochastic resonance processing step sets, with regard to a pixel signalas a processing target among the plurality of pixel signals, at leastone of a strength of the noise and a threshold value used for the binaryprocessing based on a pixel signal of the input image data correspondingto the pixel signal.
 15. An image processing method, comprising: a stepof acquiring reading image data composed of a plurality of pixel signalsby imaging an image printed based on input image data composed of aplurality of pixel signals; a stochastic resonance processing step ofexecuting a stochastic resonance processing to obtain a resultcorresponding to a result that is calculated in a case where each of theplurality of pixel signals constituting the reading image data is addednoise and subjected to a binary processing and a plurality of resultsobtained by parallelly performing above step are synthesized and theparallel number is infinite; and an output step of outputting the resultof the stochastic resonance processing, wherein the stochastic resonanceprocessing step sets, with regard to a pixel signal as a processingtarget among the plurality of pixel signals, at least one of a strengthof the noise and a threshold value used for the binary processing basedon a pixel signal of the input image data corresponding to the pixelsignal.
 16. The image processing method according to claim 15, whereinthe stochastic resonance processing step calculates a difference betweenthe plurality of pixel signals constituting the reading image data andthe plurality of pixel signals constituting the input image data to addthe noise to the difference to subject the difference to the binaryprocessing.
 17. The image processing method according to claim 15,wherein the respective pixel signals constituting the input image dataare composed of R, G, and B brightness signals; and the stochasticresonance processing step sets at least one of the strength of the noiseand the threshold value used in the binary processing based on abrightness signal obtained by synthesizing the R, G, and B brightnesssignals.
 18. The image processing method according to claim 15, whereinthe respective pixel signals constituting the reading image data arecomposed of R, G, and B brightness signals; and the stochastic resonanceprocessing step performs the stochastic resonance processing on abrightness signal obtained by synthesizing the R, G, and B brightnesssignals.
 19. The image processing method according to claim 15, whereinthe image is printed by using a plurality of printing elements; and thestochastic resonance processing step sets at least one of the strengthof the noise and the threshold value based on a pixel signals of theinput image data and the printing status of the printing elementcorresponding to a pixel signal as the processing target.
 20. The imageprocessing method according to claim 19, wherein the image is printed byan inkjet printing apparatus; and the image processing method furthercomprising a step of detecting an ink ejection status of the printingelement as the printing status.
 21. The image processing methodaccording to claim 15, wherein the image is printed by an inkjetprinting apparatus including a plurality of printing elements; and thesingular portion is a white stripe caused by ejection failure of theprinting element.
 22. The image processing method according to claim 15,wherein the noise is white noise.
 23. The image processing methodaccording to claim 15, wherein the predetermined stochastic resonanceprocessing is performed by using the following formula to calculate dataJ(x) obtained by processing input data I(x),${J(x)} = \left\{ \begin{matrix}1 & {T < {I(x)}} \\0 & {{I(x)} < {T - K}} \\{1 - {\left( {T - {I(x)}} \right)/K}} & {{T - K} \leqq {I(x)} \leqq T}\end{matrix} \right.$ where T is a threshold value to quantize the inputdata and K is a noise strength.
 24. The image processing methodaccording to claim 15, further comprising: an extraction step ofextracting a singular portion based on the result of the stochasticresonance processing.
 25. The image processing method according to claim15, further comprising: a reading step of imaging the printed image. 26.The image processing method according to claim 25, further comprising: aprinting step of printing the image based on the input image data.
 27. Anon-transitory computer-readable storage medium which stores a programfor allowing a computer to execute an image processing method, the imageprocessing method comprising: a step of acquiring reading image datacomposed of a plurality of pixel signals by imaging an image printedbased on input image data composed of a plurality of pixel signals; astochastic resonance processing step of executing a stochastic resonanceprocessing to obtain a result corresponding to a result that iscalculated in a case where each of the plurality of pixel signalsconstituting the reading image data is added noise and subjected to abinary processing and a plurality of results obtained by parallellyperforming above step are synthesized and the parallel number isinfinite; and an output step of outputting the result of the stochasticresonance processing, wherein the stochastic resonance processing stepsets, with regard to a pixel signal as a processing target among theplurality of pixel signals, at least one of a strength of the noise anda threshold value used for the binary processing based on a pixel signalof the input image data corresponding to the pixel signal.